A linear programming method for generating the most favorable weights from a pairwise comparison matrix
A linear programming method for generating the most favorable weights from a pairwise comparison matrix
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DOI：
10.1016/j.cor.2007.05.002
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发表时间：
2008-12
期刊：
影响因子：
--
通讯作者：
Yingming Wang;C. Parkan;Ying Luo
中科院分区：
文献类型：
--
作者：
Yingming Wang;C. Parkan;Ying Luo
提出了一种从两两比较矩阵中生成最有利权重的线性规划方法(LP-GFW)，该方法将数据包络分析(DEA)的变权概念引入到层次分析(AHP)的排序方案中，以一个清晰的两两比较矩阵为基础准则和方案生成最有利权重。提出的LP-GFW方法可以得到完全一致的两两比较矩阵的精确权重和不相容两两比较矩阵的近似权重，它们与Saaty的主右特征向量权重相差不太远。文中还讨论了局部最优权和保序法的集结问题。用LP-GFW方法对四个数值算例进行了检验，以说明该方法的潜在应用以及与现有的一些优先方法相比的显著优势。
This paper proposes a linear programming method for generating the most favorable weights (LP-GFW) from pairwise comparison matrices, which incorporates the variable weight concept of data envelopment analysis (DEA) into the priority scheme of the analytic hierarchy process (AHP) to generate the most favorable weights for the underlying criteria and alternatives on the basis of a crisp pairwise comparison matrix. The proposed LP-GFW method can generate precise weights for perfectly consistent pairwise comparison matrices and approximate weights for inconsistent pairwise comparison matrices, which are not too far from Saaty's principal right eigenvector weights. The issue of aggregation of local most favorable weights and rank preservation methods is also discussed. Four numerical examples are examined using the LP-GFW method to illustrate its potential applications and significant advantages over some existing priority methods.